Finding programming errors earlier by evaluating runtime monitors ahead-of-time
Bibliographic record
Abstract
Runtime monitoring allows programmers to validate, for instance, the proper use of application interfaces. Given a property specification, a runtime monitor tracks appropriate runtime events to detect violations and possibly execute recovery code. Although powerful, runtime monitoring inspects only one program run at a time and so may require many program runs to find errors. Therefore, in this paper, we present ahead-of-time techniques that can (1) prove the absence of property violations on all program runs, or (2) flag locations where violations are likely to occur. Our work focuses on tracematches, an expressive runtime monitoring notation for reasoning about groups of correlated objects. We describe a novel flow-sensitive static analysis for analyzing monitor states. Our abstraction captures both positive information (a set of objects could be in a particular monitor state) and negative information (the set is known not to be in a state). The analysis resolves heap references by combining the results of three points-to and alias analyses. We also propose a machine learning phase to filter out likely false positives. We applied a set of 13 tracematches to the DaCapo benchmark suite and SciMark2. Our static analysis rules out all potential points of failure in 50% of the cases, and 75% of false positives on average. Our machine learning algorithm correctly classifies the remaining potential points of failure in all but three of 461 cases. The approach revealed defects and suspicious code in three benchmark programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".